WalidBenzineb/E-commerce-Data-Analysis-

Data Analyst

Statistician

Microsoft Excel

Python

E-commerce Data Analysis Project

Project Overview

Dataset Description

Technical Setup

Analysis and Insights

Implementation Plan

Conclusion

Project Overview

This project analyzes an e-commerce dataset to uncover actionable insights for business growth and optimization. The analysis covers customer behavior, product performance, sales trends, and market basket analysis.

Dataset Description

The dataset consists of several CSV files:

orders.csv: Contains order information (orderId, accountId, orderDate, etc.)

order_details.csv: Links orders to products and quantities

products.csv: Product information (productId, name, category, price, etc.)

users.csv: Customer demographic data

transactions.csv: Payment transaction details

Technical Setup

Folder Structure

e-commerce-Data-Analysis/ │ ├── data/ │ ├── orders.csv │ ├── order_details.csv │ ├── products.csv │ ├── users.csv │ └── transactions.csv │ ├── scripts/ │ ├── data_preprocessing.py │ ├── rfm_analysis.py │ ├── cohort_analysis.py │ ├── product_analysis.py │ ├── sales_analysis.py │ └── main.py │ ├── outputs/ │ ├── figures/ │ └── reports/ │ ├── requirements.txt └── README.md

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Script Descriptions

data_preprocessing.py:

rfm_analysis.py:

cohort_analysis.py:

product_analysis.py:

sales_analysis.py:

main.py:

How to Use

Clone the repository

Install required packages: pip install -r requirements.txt

Extract the CSV data files in the data/ directory.

Run the main analysis script: python scripts/main.py

View the results in the outputs/ directory:

Requirements

The project requires the following Python packages:

pandas

matplotlib

seaborn

scikit-learn

mlxtend

Analysis and Insights

Customer Demographics and Segmentation

Graph Analysis:

Age Distribution:

Right-skewed distribution with a significant peak in the 65+ age group.

Notable dip in the 18-24 age group, followed by a rise in the 25-34 group.

Relatively consistent customer numbers in the 35-64 age ranges.

Customer Segments:

"Average Customers" form the largest segment, followed by "Lost Customers".

"Best Customers" and "New Customers" are smaller but crucial segments.

Key Insights and Actions:

Develop an "Easy Shop" interface for the 65+ demographic, emphasizing accessibility features.

Create targeted campaigns for the 25-34 age group, focusing on mobile and social media integration.

Implement a stratified loyalty program to move customers from "Average" to "Best" segments.

RFM Analysis and Customer Retention

Graph Analysis:

RFM Distribution:

Cohort Analysis:

Key Insights and Actions:

Implement a "Second Purchase" campaign targeting customers 150 days after their first purchase.

Develop a VIP program for high monetary value customers (top 10% of spenders).

Create a re-engagement series for customers approaching the 180-day mark since last purchase.

Product Performance and Cross-Selling

Graph Analysis:

-Top Product Categories:

Relatively even distribution among the top 10 categories.

Tuna, Ball, and Chips are the top three categories, each with sales just over $1.2 million.

Gradual decrease in sales from the top to the 10th category, but all exceed $1 million in sales.

Association Rules:

Key Insights and Actions:

Sales Trends and Payment Preferences

Graph Analysis:

Monthly Sales Trend:

Payment Methods Distribution:

Key Insights and Actions:

Impementation Plan

Q3 2024:

Launch the "Easy Shop" interface for 65+ demographic

Develop and implement the stratified loyalty program

Q4 2024:

Create and launch the "Second Purchase" campaign

Prepare for the January 2025 promotional campaign

Q1 2025:

Implement the cross-category bundling strategy

Roll out the recommendation system based on association rules

Q2 2025:

Introduce new payment incentives to balance payment methods

Launch the re-engagement series for at-risk customers

Conclusion

This comprehensive analysis provides a data-driven foundation for strategic decision-making in our e-commerce business. By leveraging these insights and implementing the suggested actions, we can expect improvements in customer retention, average order value, and overall sales performance. The technical approach demonstrated here ensures that our strategies are based on robust data analysis and can be continually refined as new data becomes available.